Create Index

Creates and returns an EncryptedIndex instance based on the provided configuration.

client.create_index(
    index_name,
    index_key,
    index_config,
    embedding_model,
    max_cache_size
)

Parameters

Parameter	Type	Default	Description
`index_name`	`str`	-	Unique name for the index
`index_key`	`bytes`	-	32-byte encryption key
`index_config`	`IndexConfig`	-	Index configuration object
`embedding_model`	`str`	`None`	(Optional) Embedding model identifier

Returns

An instance of EncryptedIndex configured with the provided parameters.

Exceptions

Error

Throws if the index name already exists on the server.
Throws if the index configuration is invalid or missing required parameters.
Throws if the encryption key is not exactly 32 bytes.
Throws if the embedding model is not supported by the server.

Network/API Errors

Throws if the API request fails due to network issues.
Throws if the server returns an HTTP error status.
Throws if authentication fails (invalid API key).

Example Usage

Basic usage of the create_index method to create an IVF-Flat index:

from cyborgdb import Client, IndexIVFFlat, generate_key

# Initialize client
client = Client('https://localhost:8000', 'your-api-key')

# Generate a secure encryption key
index_key = generate_key()

# Create a basic IVF-Flat index
config = IndexIVFFlat(dimension=384, n_lists=100, metric="cosine")
index = client.create_index('documents', index_key, config)

IVFPQ Index with Embedding Model

# Create index with embedding model and cache
config = IndexIVFPQ(dimension=768, n_lists=50, pq_bits=8, pq_dim=64, metric="cosine")
index = client.create_index(
    index_name='example_index_name',
    index_key=index_key,
    index_config=config,
    embedding_model='all-MiniLM-L6-v2'
)

Introduction

Client

Encrypted Index

Index Configuration

Parameters

Returns

Exceptions

Example Usage

IVFPQ Index with Embedding Model

​Parameters

​Returns

​Exceptions

​Example Usage

​IVFPQ Index with Embedding Model

Parameters

Returns

Exceptions

Example Usage

IVFPQ Index with Embedding Model